Cancer Prevention Using Machine Learning, Nudge Theory and Social Impact Bond

Daitaro Misawa, Jun Fukuyoshi and Shintaro Sengoku
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Daitaro Misawa: Department of Innovation Science, School of Environment and Society, Tokyo Institute of Technology, Tokyo 152-8850, Japan
Jun Fukuyoshi: Cancer Scan, Co., Ltd., Tokyo 141-0031, Japan
Shintaro Sengoku: Department of Innovation Science, School of Environment and Society, Tokyo Institute of Technology, Tokyo 152-8850, Japan

IJERPH, 2020, vol. 17, issue 3, 1-11

Abstract: There have been prior attempts to utilize machine learning to address issues in the medical field, particularly in diagnoses using medical images and developing therapeutic regimens. However, few cases have demonstrated the usefulness of machine learning for enhancing health consciousness of patients or the public in general, which is necessary to cause behavioral changes. This paper describes a novel case wherein the uptake rate for colorectal cancer examinations has significantly increased due to the application of machine learning and nudge theory. The paper also discusses the effectiveness of social impact bonds (SIBs) as a scheme for realizing these applications. During a healthcare SIB project conducted in the city of Hachioji, Tokyo, machine learning, based on historical data obtained from designated periodical health examinations, digitalized medical insurance receipts, and medical examination records for colorectal cancer, was used to deduce segments for whom the examination was recommended. The result revealed that out of the 12,162 people for whom the examination was recommended, 3264 (26.8%) received it, which exceeded the upper expectation limit of the initial plan (19.0%). We conclude that this was a successful case that stimulated discussion on potential further applications of this approach to wider regions and more diseases.

Keywords: disease prevention; machine learning; nudge theory; medical data; social impact bond (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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